Citation
Imai, Kosuke, Gary King, and Elizabeth A. Stuart. (2008). “Misunderstandings among Experimentalists and Observationalists about Causal Inference.” Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 171, No. 2 (April), pp. 481-502. Reprinted in Field Experiments and their Critics, D. Teele ed. (2013), New Haven: Yale University Press, Forthcoming.
Abstract
We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference in experimental and observational research. These issues concern some of the most basic advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization, and matching after treatment assignment to achieve covariate balance. Applied researchers in a wide range of scientific disciplines seem to fall prey to one or more of these fallacies, and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the key estimation errors in making causal inferences. We then show how this decomposition can help scholars from different experimental and observational research traditions better understand each other’s inferential problems and attempted solutions.
Related Information
- In July 2009, this paper was recognized by Thomson Reuters’ ScienceWatch as New Hot Paper, for the most-cited paper in the field of Economics & Business in the last two months among papers published in the last year.
- You may also be interested in Ho, Daniel, Kosuke Imai, Gary King, and Elizabeth A. Stuart. (2007). “Matching as Nonparametric Preprocessing for Improving Parametric Causal Inference.” Political Analysis, Vol. 15, No.3 (Summer), pp. 199-236.